A Computational Pathology Model to Predict Docetaxel Benefit in Localized High-Risk and Metastatic Prostate Cancer

作者：Sebastian Medina, Naoto Tokuyama, Kamal Hammouda, Tilak Pathak, Tuomas Mirtti, Pingfu Fu, Shilpa Gupta, Priti Lal, Howard M. Sandler, Rohann Correa, Susan Chafe, Amit Shah, Jason A. Efstathiou, Karen E. Hoffman, Michael Straza, M.A. Hallman, Richard C. Jordan, Stephanie L. Pugh, Christopher J. Sweeney, Anant Madabhushi · 发表于：Clinical Cancer Research · 年份：2025 · DOI：10.1158/1078-0432.ccr-25-3327 · 被引用次数：2 · 研究领域：Prostate Cancer Treatment and Research、Prostate Cancer Diagnosis and Treatment、Statistical Methods in Clinical Trials

PURPOSE: Docetaxel improves survival in metastatic hormone-sensitive prostate cancer (mHSPC) and high-risk localized disease, but benefits vary substantially among patients. Without predictive biomarkers, clinicians cannot identify patients who will benefit, exposing many to unnecessary toxicity. We developed and validated an artificial intelligence-based pathology image classifier (APIC) to predict docetaxel benefit. EXPERIMENTAL DESIGN: We analyzed digitized hematoxylin and eosin-stained biopsy specimens from two phase 3 trials: CHAARTED (286/790 patients with mHSPC) and NRG/RTOG 0521 (350/563 patients with high-risk localized disease). APIC used features capturing tumor-immune spatial interactions and nuclear heterogeneity. We evaluated the predictive value of APIC for docetaxel benefit on overall survival (OS) and castration resistance using Cox proportional hazards with interaction terms. RESULTS: In CHAARTED, APIC-positive patients (56.7%) showed significant OS improvement with docetaxel [HR, 0.52; 95% confidence interval (CI), 0.31-0.85; P = 0.008] and delayed castration resistance (HR, 0.48; 95% CI, 0.33-0.71; P < 0.001), whereas APIC-negative patients (43.3%) showed no benefit (HR, 1.31; 95% CI, 0.71-2.44; P = 0.39). Treatment-APIC interactions were significant (P = 0.022 and P = 0.031). In NRG/RTOG 0521, APIC-positive patients (44.7%) demonstrated survival benefit (HR, 0.49; 95% CI, 0.26-0.92; P = 0.023), whereas APIC-negative patients (55.3%) showed no benefit. Tre...